Jangra Manisha, Dhull Sanjeev Kumar, Singh Krishna Kant, Singh Akansha, Cheng Xiaochun
Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana India.
Faculty of Engineering and Technology, Jain (Deemed-to-be University), Bengaluru, India.
Complex Intell Systems. 2023;9(3):2685-2698. doi: 10.1007/s40747-021-00371-4. Epub 2021 Apr 26.
The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.
心律失常的定期监测和准确诊断至关重要，可降低因心血管疾病（CVD）如中风或心脏骤停导致的死亡率。本文提出了一种用于心律失常分类的新型卷积神经网络（CNN）模型。与传统的CNN模型相比，该模型有以下改进。首先，多通道模型可以连接频谱和空间特征图。其次，结构单元由深度可分离卷积层、激活层和批量归一化层组成。该结构单元能有效利用网络参数。此外，基于顺序模型的全局优化算法（SMBO），使用Hyperopt库对超参数进行优化。这些改进使网络在心律失常分类方面更高效、准确。按照面向受试者的患者间和面向类别的患者内评估协议，使用十折交叉验证对所提出的模型进行评估。我们的模型在室性早搏（VEB）和室上性早搏（SVEB）分类中分别达到了99.48%和99.46%的准确率。该模型与现有最先进的模型进行了比较，并显示出显著的性能提升。
